ISSN (P) : 2086-4264 ISSN (E) : 2581-2343
Dewan Redaksi Jurnal Riset Akuntansi & Komputerisasi Akuntansi
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Univeristas Trunojoyo, Madura
Prof. Dr. Hj. Nunuy Nur Afiah, SE.,M.S. Ak.
Univeristas Padjajaran
Dr. Sugiyarti Fatma Laela, M. Buss. Acc.
CMA,
Institut Tazkia
Dr. Icuk Rangga Bawono, SH.,SE.,M.Si.,MH.,Ak.,CA
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(Universitas Buana Perjuangan Karawang)
Gafar Hafiz Sagala, S.Pd.,M.Sc
Universitas Negeri Medan
Andi Manggala Putra, SE., M.Sc.
Universitas Pembangunan Nasional
"Veteran" Jakarta
Mohammad Iqbal Firdaus, SE., M.Ak.
Universitas Negeri Malang
Purnama Putra, SE.,M.SiUniversitas Islam 45, Indonesia
Hadi Mahmudah, SE.,M.ScUniversitas Islam 45, Indonesia
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50
Sales Growth, Company Size, Profitability, and Non-Debt Tax Shield on Capital Structure In Food and Beverage Sub-Sector
Companies Listed on The Indonesia Stock Exchange
1Nicholas Renaldo*
2Christian Rusli
3Suhardjo
4Suharti
5Tandy Sevendy
1,2,3,4,5Institut Bisnis dan Teknologi Pelita Indonesia
ABSTRACT
The primary objective of this study is to assess the impact of sales growth, company size, profitability, and non- debt tax shield on the capital structure of consumer goods companies operating within the food and beverage sub-sector and listed on the Indonesia Stock Exchange during the period from 2017 to 2020. The research employed secondary data and adopted a purposive sampling approach, resulting in a sample size of 38 companies. Data analysis encompassed quantitative descriptive techniques and various calculations carried out using SmartPLS software. The study's findings indicate that only the non-debt tax shield exerts a significant influence on capital structure, whereas sales growth, company size, and profitability do not exhibit a significant impact on capital structure.
Keywords: Sales Growth, Company Size, Profitability, Non-Debt Tax Shield, Capital Structure ABSTRAK
Tujuan utama dari penelitian ini adalah untuk menilai dampak pertumbuhan penjualan, ukuran perusahaan, profitabilitas, dan non-debt tax shield terhadap struktur modal pada perusahaan barang konsumsi yang beroperasi pada subsektor makanan dan minuman dan terdaftar di Bursa Efek Indonesia. selama periode 2017 hingga 2020. Penelitian ini menggunakan data sekunder dan menggunakan pendekatan purposive sampling, sehingga menghasilkan jumlah sampel sebanyak 38 perusahaan. Analisis data meliputi teknik deskriptif kuantitatif dan berbagai perhitungan dilakukan dengan menggunakan software SmartPLS. Temuan penelitian menunjukkan bahwa hanya non-debt tax shield yang memberikan pengaruh signifikan terhadap struktur modal, sedangkan pertumbuhan penjualan, ukuran perusahaan, dan profitabilitas tidak menunjukkan pengaruh signifikan terhadap struktur modal.
Kata Kunci: Pertumbuhan Penjualan; Ukuran Perusahaan; Profitabilitas; Non-Debt Tax Shield;
Struktur Modal
INTRODUCTION
Companies within the consumer goods industrial sector are anticipated to continue playing a pivotal role in propelling future manufacturing and contributing to national
economic growth. Ministry of Industry data underscores the substantial impact of the food and beverage industry on the non-oil and gas industry's GDP throughout the period spanning 2014 to 2020. During this period, it accounted for a substantial share, with figures such as 9.49% in 2014, 7.54% in 2015, 8.33% in 2016, 9.23% in 2017, 7.91% in 2018, 7.83% in 2019, and 9.23% in 2020. These impressive performance results establish this sector as the leading contributor to industrial GDP compared to other subsectors, even prior to the pandemic. Moreover, it exhibited an increasing percentage of contribution to the national GDP. Furthermore, the investment realization in the domestic food and beverage industry sector has been on a steady rise.
Data shows that GDP growth is experiencing fluctuating movements. Consumer goods industry GDP growth in recent years has decreased in 2015, where this year it was at its lowest point, namely 7.54%, then again in 2018-2019, however, in 2020 it increased from the previous year to 9.32%. The consumer goods sector index maintains its position as the most resilient index, experiencing the least amount of correction. According to statistics from the Indonesian Stock Exchange (BEI), the consumer goods sector index has seen a correction of only 12.01%. This correction is notably lower compared to the Composite Stock Price Index (IHSG), which has undergone a more significant decline of 23.13% since the beginning of the year or year-to-date (YTD). It's worth noting that the weakening observed in the consumer goods index is comparatively less severe when compared to other sectoral indices. For instance, the property and real estate sector index has witnessed a decline of 32.84%, while the miscellaneous industry sector index experienced a correction of 31.81%, as reported by https://investasi.kontan.co.id in 2021.
The weakening of the JCI in 2020 affected all sectoral indices, including the consumer goods index, but was not as severe as property and real estate and various industrial sector indices. Many consumer goods companies posted low margins due to increases in raw materials. The movement of the non-cyclical consumer sector stock index or primary consumer goods sector has appeared sluggish since the beginning of the year. Data from the Indonesian Stock Exchange (BEI) shows that this sector fell 11.29% year to date (ytd).
The decline was the deepest after the property and real estate sectors. Many primary consumer goods issuers are currently posting lower margins. This is inseparable from the increase in raw materials such as the price of crude palm oil (CPO), oil, wheat from the second semester of 2021 to the first semester of 2021 (https://investasi.kontan.co.id, 2021).
The risks surrounding the consumer sector currently still come from high prices of soft commodities, although it is projected that soft commodity prices will tend to decline in the future (https://investasi.kontan.co.id, 2022).
Capital plays a pivotal role in fostering the sustainability of a company, particularly in industries like consumer goods. The concept of capital structure, as explored by Renaldo, Junaedi, et al. in 2023 and Renaldo, Sally, et al. in 2023, takes center stage in a range of corporate activities. The capacity to determine the optimal capital structure holds a direct influence over a company's financial standing. Capital structure represents the outcome of financing decisions, involving choices regarding the use of debt and equity to support a company's operations. It encompasses a mix of permanent and short-term debt, long-term debt, preferred stock, and common stock, as outlined by Sartono in 2016.
Capital structure refers to the breakdown of a company's financial resources, specifically the proportions between its long-term debt and its equity, which serve as sources of funding. A commonly used metric to gauge capital structure is the Debt-to- Equity Ratio (DER). This ratio assesses a company's capacity to meet its debt obligations with the backing of its own equity. An optimal capital structure is one that strikes the right balance between risk and return, ultimately maximizing shareholder value, as elucidated by Margaretha in 2011. A company with an inefficient capital structure may end up burdened with substantial debt, underscoring the importance of establishing an optimal structure as a fiduciary responsibility to stakeholders. Maximizing the profitability and prosperity of company owners can be done by handing over capital management from stakeholders who have a share in funding to company management (Riyanto, 2013). The higher use of debt causes an increase in company risk, a high increase in risk will cause creditors to also set high interest rates on their loans to the company (Brigham and Houston, 2014).
Capital structure can be influenced by a range of factors encompassing sales stability, asset composition, operating leverage, growth rate, profitability, tax considerations, oversight, management approach, creditor and consultant attitudes, company size, risk exposure, market conditions, internal financial circumstances, and financial flexibility.
These factors form the foundation upon which managerial decisions are made. A well- optimized capital structure plays a pivotal role in building investor confidence, ultimately attracting more investment, as articulated by Riyanto in 2013.
Sales growth signifies the year-on-year increase in a company's total sales. The extent of this sales growth has an influence on the capital structure, as companies often opt to limit their use of debt to mitigate risks stemming from business uncertainties. This strategic choice affects the overall composition of the company's capital. Company size, on the other hand, is an indicator of a company's magnitude, typically gauged by its total wealth or the overall assets it possesses. Total assets represent the wealth or assets owned by a company over a specific period, as defined by Kasmir in 2017. Profitability, in the context of this discussion, relates to a company's capability to generate profits relative to its sales, total assets, and equity, as described by Sartono in 2016.
The NDTS represents a tax protection mechanism that strongly encourages the use of debt, particularly for companies with substantial taxable income. However, it's worth noting that the tax advantages of debt decrease when other tax-related factors change, such as an increase in depreciation, as explained by Suripto in 2015. Non-debt tax shields encompass costs associated with tax benefits unrelated to debt, specifically depreciation and amortization. Companies possessing a high level of non-debt tax shield tend to employ lower levels of debt. This is because their cash flow serves as the primary capital for running their operations. Conversely, when the non-debt tax shield diminishes, companies are more inclined to take on larger debts. According to the Trade-Off Theory, increasing debt levels can provide companies with protection from interest costs on debt, ultimately leading to reduced taxable profits.
Furthermore, in researching research that had been previously carried out, it was found that there were differences between one researcher and another and did not reach the same conclusions from the results of their research. Nugroho in 2014 asserts that there is a significant positive impact of sales growth on capital structure. However, Puspawardhani in 2015 contradicts this by suggesting that sales growth has no significant negative influence on capital structure. Additionally, both Nugroho in 2014 and Marfuah in 2017 find that sales growth does not have a significant effect on capital structure.
Research by Setyawan in 2016 asserts that company size exhibits a significant positive impact on capital structure. In contrast, Nugroho in 2014 suggests that company size has a significant negative effect on capital structure. However, Barqoya's research in 2019 indicates that company size has an insignificant negative influence on capital structure.
Additionally, Setyawan's research in 2016 concludes that profitability has a positive effect
on capital structure, while Barqoya's study in 2019 suggests that profitability has a negative effect on capital structure.
Divergent research findings exist regarding the impact of the NDTS on capital structure. Fachri in 2019 asserts that the NDTS yields a positive effect on capital structure, highlighting its significant and positive influence. This effect is attributed to companies with substantial fixed assets, which tend to employ more debt, utilizing these assets as collateral. In contrast, Kamil in 2021 contends that the NDTS has a insignificant effect on capital structure.
LITERATURE REVIEW Signaling Theory
Signaling theory, as discussed by Chandra et al. in 2018, Nyoto et al. in 2023, and Renaldo & Murwaningsari in 2023, explains how companies utilize information to convey either positive or negative signals to their stakeholders. Corporate executives, armed with superior knowledge about their firms, are incentivized to communicate this information to potential investors, aiming to enhance the company's value through informative reporting, as noted by Scott in 2015. Signaling theory elucidates the motivation behind companies' willingness to provide financial information to external parties. This motivation stems from the presence of information asymmetry between the company and external stakeholders, such as investors and creditors. The company possesses more comprehensive insights into its own operations and future prospects. Consequently, the lack of information for external parties leads them to adopt a cautious approach, often offering a lower valuation for the company. Companies can mitigate this information asymmetry by transmitting signals to external parties, as articulated by Arifin in 2017. Signaling theory revolves around the concept of information signals that investors rely on to assess and make decisions regarding their investments in a particular company. It is grounded in practical financial theory, which emphasizes how information can impact changes in behavior based on information.
One form of information that can serve as a signal is an announcement issued by a company. Such announcements have the potential to influence the fluctuations in the stock prices of the company that made the announcement, as explained by Suwardjono in 2015.
Trade off Theory
The Trade-Off Theory, a model of capital structure, posits that a company's capital makeup represents a delicate equilibrium between the advantages gained from debt usage
and the associated drawbacks of financial distress and agency costs. This theory operates on the premise of a trade-off, weighing the benefits and disadvantages of incorporating debt into a company's financial structure. Debt introduces interest expenses that can yield tax savings, as these expenses can be deducted from income, thereby reducing pre-tax profits, as outlined by Brigham and Houston in 2014. The Trade-Off Theory model posits that a company's capital structure results from a trade-off between the tax advantages gained from using debt and the potential agency costs and financial distress it may entail.
Debt can indeed increase a company's value, but only up to a certain limit. Once that threshold is exceeded, excessive debt can reduce the company's overall value. This model aims to find the optimal capital structure by carefully balancing the benefits of debt with the costs associated with financial distress and agency problems, as explained by Atmaja in 2013.
Pecking Order Theory
Pecking order is a hierarchy of funding that starts from the retained earnings balance, then followed by debt or loans and finally using external funding, namely selling shares to obtain funds (Gitman and Zutter, 2015). Pecking order theory as a hierarchy of company priorities when it needs funding to increase capital. The primary priority is to utilize internal funds, specifically retained earnings. Subsequently, the next option is to employ additional debt, and finally, consider issuing new shares (Brigham and Houston, 2014). The Pecking Order Theory posits that companies with high profitability tend to have low levels of debt. This is because highly profitable companies possess ample internal funding sources. Regarding capital structure in the Pecking Order Theory, there isn't an optimal structure; instead, companies adhere to a hierarchy when sourcing funds. Companies typically prioritize using internal funds for their projects, as outlined by Sudana in 2013.
The Pecking Order Theory suggests that companies have a preference for utilizing retained earnings to cover dividends and fund new investments. Retained earnings are considered the top priority, followed by debt, and external equity is viewed as the last resort.
According to this theory, there's an expected negative correlation between profits and debt ratios. Furthermore, the Pecking Order Theory suggests that the observed company's capital structure is positively related to factors like size, growth, and asset wealth, as highlighted by Harjito and Martono in 2013.
A company that maintains relatively stable sales enjoys the benefit of having a consistent cash flow, which allows it to employ more debt compared to a company experiencing sales fluctuations. Firms with stable growth profiles are in a more secure position, capable of securing larger loans, and managing higher fixed expenses in comparison to businesses with erratic sales patterns. Public companies, due to stable demand for their products or services, have historically been able to use more financial leverage than industrial companies (Sartono, 2016). Companies with stable sales patterns are better positioned to secure loans with confidence and manage higher fixed costs compared to businesses experiencing sales volatility. This is due to the fact that the requirement for funds to support sales growth correlates with the potential increase in a company's earnings per share, which can be achieved through the use of debt. Rapidly growing companies often resort to a higher level of debt to finance their operations, as noted by Brigham and Houston in 2014. Sales growth serves as an indicator of the potential to enhance a company's earnings per share through the use of debt. This growth will increase profits before interest and tax, so that company funding that uses debt with interest charges as a tax deduction can increase net profit so that earnings per share also increase. Pecking order theory has a positive signal, namely, companies with high growth will expand using debt (Christianti, 2008).
H1: Sales growth has a positive effect on capital structure
Larger companies tend to leverage more debt compared to smaller companies. This propensity stems from the relative ease with which larger firms can secure loans, thanks to their typically higher leverage levels compared to smaller counterparts. Additionally, larger companies generally exhibit lower bankruptcy rates in contrast to smaller ones, contributing to an increase in their capital structure due to their greater reliance on debt.
The larger the company, the more information it will disclose in its annual report, both financial and non-financial information. There are several reasons behind the tendency of large companies to provide more extensive information in their reports compared to small companies. Firstly, larger companies often benefit from reduced information production costs or fewer competitive loss concerns compared to their smaller counterparts. Secondly, the expansive shareholder base of large companies may necessitate increased disclosures to meet shareholder demands. Thirdly, large companies often have the capacity to recruit
highly qualified personnel who are adept at implementing sophisticated reporting systems.
Lastly, managers of small companies might believe that excessive information disclosure could potentially harm the competitive edge of the company, as noted by Ulum in 2013.
H2 : Company size has a positive effect on capital structure
Companies that achieve a substantial return on investment tend to employ less debt. A robust return rate diminishes the company's reliance on external sources, as high profits enable the firm to secure the majority of its funding from retained earnings, as highlighted by Brigham and Houston in 2014. Companies with substantial retained earnings, as indicated by Sartono in 2016, possess the means to cover a significant portion of their financial needs. This reliance on retained earnings signifies a company's capacity to sustain its operational activities without resorting to debt. When a company has ample retained earnings, it typically opts to utilize them as a financing source before turning to debt. Profitability serves as a measure of a company's ability to generate profits. According to the Pecking Order Theory, companies favor internal financing, and those with high profitability levels prioritize internal funding. Consequently, when a company's profitability increases, its reliance on debt typically decreases, thereby affecting its capital structure by reducing its debt usage.
H3 : Profitability has a negative effect on capital structure
The NDTS factor is another factor of capital structure, particularly significant for companies that prefer to minimize their debt exposure to mitigate bankruptcy risks. The NDTS represents tax savings arising from the depreciation of tangible assets, which can impact the composition of capital structure. This NDTS takes the form of accounting for depreciation and amortization expenses against the profit and loss statement. Depreciation and amortization function as incentives to reduce debt because depreciation and amortization are cash flow sources of company capital so they can reduce funding from debt. Tax benefits like depreciation deductions and investment tax credits offer avenues for reducing taxes, distinct from the interest on debt. In striving for tax efficiency, businesses can maximize profits and tax relief through government-provided tax incentives, commonly referred to as a non-debt tax shield. The greater the tax savings derived from
the non-debt tax shield, the more post-tax profits become available for funding, making it a relevant factor for management when determining capital structure. Based on Pecking Order Theory, companies endowed with a substantial non-debt tax shield tend to reduce their reliance on debt when their profits increase. This is because a portion of these profits serves as an internal funding source. The concept of the Non-Debt Tax Shield, as introduced by De Angelo and Masulis in 1980, can be considered a pioneering contribution to the literature. This concept encompasses various forms of tax protections, including depreciation deductions, depletion allowances, and investment tax credits. These protections essentially serve as substitutes for the tax advantages associated with debt financing for companies. Consequently, a company with a higher Non-Debt Tax Shield value tends to opt for lower levels of debt. This prediction is supported by the findings of numerous empirical studies. In this study, the Non-Debt Tax Shield is quantified by comparing the total depreciation to the total assets of the company.
H4 : Non-debt tax shield has a negative effect on capital structure
Based on the previous description, the influence of sales growth, company size, profitability and non-debt tax shield on capital structure can be described with the following chart:
Figure 1 Thinking Framework
RESEARCH METHOD Place and Time of Research
The research was conducted on food and beverage sub-sector companies listed on the IDX, with data obtained through the website www.idx.co.id. This study uses the IDX
Profitabilitas (X3) Non-Debt Tax Shield (X4)
Ukuran Perusahaan (X2)
Struktur Modal (Y) Pertumbuhan Penjualan (X1)
H1 (+) H2 (+)
H3 (-)
H4 (-)
as a research site, because this official site has complete data on companies going public.
The research was conducted from July 2022 to December 2022.
Population and Sample
The population for this study comprises companies in the food and beverage sub- sector that were listed on the Indonesia Stock Exchange (IDX) during the period from 2017 to 2020. Specifically, there were a total of 80 companies falling within the food and beverage sub-sector that were listed on the IDX during this time frame. Companies in the Food &
Beverages Sub-sector (D2) consist of Liquor (D211), Soft Drinks (D212), Processed Dairy Products (D221), Processed Foods (D222), Fish, Meat & Poultry Products (D231), Plantation
& Food Crops (D232). The research employed a purposive sampling method, which involves the deliberate selection of non-random samples based on specific criteria aligned with the research objectives. In this case, 38 food and beverage sub-sector companies listed on the IDX during the 2017-2020 period were selected as samples for the study.
Table 1
Research Sample Criteria
No Information Sample
1 Food and beverage sub-sector companies listed on the IDX in 2017-2020 80 2 Food and beverage sub-sector companies that IPO under 2017 (37) 3 Food and beverage sub-sector companies that were suspended in 2017-2020 (3) 4 Companies in the food and beverage sub-sector whose financial reports
were not found
(2)
5 Food and beverage sub-sector companies as samples 38
Operational and Measurement of Research Variables
In research there are dependent variables and independent variable.
Table 2
Operational Definition and Variable Measurement
Variable Indicator Scale
Capital Structure (Y) DER = Total Debt
𝑇𝑜𝑡𝑎𝑙 𝐸𝑞𝑢𝑖𝑡𝑦 x 100% (Riyanto, 2013) Ratio
Sales Growth (X1) Sales Growth=Sales (𝑡)−Sales (𝑡−1)
Sales (𝑡−1) x 100% (Harahap, 2015) Ratio
Company Size (X2) SIZE= Log (total aset) (Murhadi, 2015). Ratio Profitability (X3) ROA =Earnings after tax
Total Asset x100% (Hery, 2017) Ratio
Non-Debt Tax Shield
(X4) NDTS = Deprecation
Total Asset (Harahap, 2015) Ratio
Data Analysis Technique
The research methodology in this study involves the use of multiple linear regression analysis, descriptive analysis, classical assumption testing, and hypothesis testing, with SPSS as the primary analytical tool. In cases where research data does not conform to a normal distribution, this study resorts to Partial Least Squares (PLS) as an alternative analysis tool. This research employs secondary data obtained from published annual financial reports and independent auditor reports of companies operating in the food and beverage sub-sector, which were listed on the Indonesian Stock Exchange (IDX) during the period from 2017 to 2020. These data sources were accessed directly through www.idx.co.id.
Descriptive Analysis
Descriptive analysis, as outlined by Sugiyono in 2019, is an analytical approach aimed at constructing systematic, objective, and precise portrayals, depictions, or representations of facts, attributes, and interrelationships within the phenomena under investigation or research. In the context of data analysis, descriptive analysis serves the purpose of offering a comprehensive overview of key statistics such as minimum and maximum values, mean, and standard deviation.
Multicollinearity Test
Multicollinearity testing, as described by Priyatno in 2012, is a procedure employed to detect situations where a regression model exhibits a strong or nearly perfect correlation among its independent variables. In a well-constructed regression model, there should be no significant correlation among the independent variables. To determine the presence of multicollinearity in the regression model, the tolerance and variance inflation factor (VIF) values are examined. The general guideline for identifying multicollinearity is as follows:
if the tolerance value exceeds 10% (0.1) and the VIF value is below 10, it can be concluded that there is no multicollinearity among the independent variables in the regression.
Heteroscedasticity Test
Heteroscedasticity, as explained by Priyatno in 2012, occurs within a regression model when there is unequal variance among the residuals observed from one data point to another. Various methods are available for assessing heteroscedasticity, and one such method is the Glejser Test. In this test, you conduct a regression analysis using the absolute values of the residuals as the dependent variable and the independent variables. The presence or absence of heteroscedasticity is determined by examining the significance probability, typically at a 5% confidence level. If the significance level is greater than 0.05, it indicates the absence of heteroscedasticity, while a significance level below 0.05 suggests the presence of heteroscedasticity symptoms.
Autocorrelation Test
This test is employed to assess the presence of strong correlations among residuals.
The decision-making process involves examining the Asymp. Sig (2-tailed) value. If the Asymp. Sig (2-tailed) value exceeds the significance level of 0.05, it can be concluded that no autocorrelation exists. The Durbin-Watson Test offers a more definitive conclusion when addressing potential issues, providing either a clear outcome or an inconclusive result, as outlined by Ghozali in 2013.
Multiple linear regression
Multiple linear regression analysis, as described by Priyatno in 2012, is a statistical method employed to quantify the extent of the impact of two or more independent variables on a single dependent variable, aiming to predict the dependent variable using these independent variables. The fundamental equation for multiple linear regression is as follows:
Y = α + β1X1 + β2X2 + β3X3 + β4X4 + 𝑒 Information:
Y = Capital Structure
α = Constant
X1 = Sales Growth
X2 = Company Size
X3 = Profitability
X4 = Non-Debt Tax Shield
β1β2, . . βn = Coefficient
F Test
The F-test is employed to determine the linearity of the regression model. This is accomplished by comparing the significance level (sig F) obtained from the test results with the predetermined significance value utilized in the research. In this research, the method for simultaneously testing independent variables, as outlined by Ghozali in 2013, is employed. If the sig F level is less than or equal to 0.05, the hypothesis is accepted, indicating that the regression model is linear. Conversely, if the sig F level is greater than or equal to 0.05, the hypothesis is rejected, signifying that the regression model is not linear.
Coefficient of Determination (R2)
The coefficient of determination, represented as R², is used to measure how effectively an independent variable can explain variations in the dependent variable. R² values fall within the range of zero to one. To gauge the results of the coefficient of determination, the adjusted R² value is considered, where a higher adjusted R² suggests a better fit for the regression model utilized in the research study. This calculation follows the formula provided by Ghozali in 2013, which is expressed as:
KD = r2 x 100%
Information:
KD = Coefficient of Determination r2 = Correlation Coefficient
Hypothesis Test (t Test)
Hypothesis testing plays a vital role in assessing the significance of the influence of individual independent variables on explaining the dependent variable in this research. To conduct hypothesis testing, a statistical t-test is employed to evaluate the partial impact of each independent variable on the dependent variable. The t-test involves the computation
of the t-value, followed by a comparison with a predetermined t-table, following the guidelines provided by Ghozali in 2013. If the t-count surpasses the t-table value or if the significance level (probability) is below 0.05, it indicates that the independent variable partially affects the dependent variable. Conversely, if the t-count is lower than the t-table value or if the significance level (probability) exceeds 0.05, it implies that the independent variable does not have a partial effect on the dependent variable.
Testing Using SmartPLS
This research primarily employs multiple linear regression analysis for data analysis.
However, if the data does not adhere to the classical assumptions of normal distribution, the hypothesis testing process will proceed using Partial Least Squares (PLS) as an alternative analytical tool.
RESULT AND DISCUSSION Descriptive Statistic
The descriptive statistical analysis results for the variables used in this study are presented in the following table:
Table 3
Descriptive Statistics
Mean Median Observed min Observed max Standard deviation
ROA 5.327 3.862 -37.670 60.717 12.873
DER 112.864 102.817 -3063.853 1721.064 343.865
NDTS 0.254 0.243 0.001 0.869 0.131
SIZE 6.678 6.622 4.958 8.254 0.632
∆Penjualan 7.454 7.163 -85.495 93.698 22.916 Source: Processed data, 2023
Based on the provided table, we can observe that there are a total of 190 data points for all variables. The Capital Structure Data (DER) (Y) has a minimum value of -3063.853, a maximum value of 1721.064, a mean value of 112.864, and a standard deviation value of 343.865. Sales Growth (X1) has a minimum value of -85.495, a maximum value of 93.698, a mean value of 7.454, and a standard deviation value of 22.916. Firm size (X2) has a minimum value of 4.958, a maximum value of 8.254, a mean value of 6.678, and a standard deviation value of 0.632. Profitability (ROA) (X3) has a minimum value of -37.670, a maximum value of 60.717, a mean value of 5.327, and a standard deviation value of 12.873.
Non-Debt Tax Shield (X4) has a minimum value of 7, a maximum value of 92, a mean value of 41.43, and a standard deviation value of 22.04.
Multicollinearity Test
The results of the multicollinearity test, conducted using the Variance Inflation Factor (VIF) test, indicate that the VIF values for the variables Sales Growth (X1), Company Size (X2), Profitability (ROA) (X3), Non-Debt Tax Shield (NDTS) (X4), and Capital Structure (DER) (Y) are all below 10.00. A VIF value less than 10 and a tolerance value greater than 0.1 suggest that the data is not affected by multicollinearity.
Heteroscedasticity Test
The Breusch-Pagan Test is employed to detect the presence or absence of heteroscedasticity. If the significance probability is greater than 0.05, it suggests the absence of heteroscedasticity. Conversely, if it is less than 0.05, it indicates the presence of heteroscedasticity symptoms. In this case, the obtained P value is 0.000, which is smaller than 0.05, signifying the presence of symptoms of heteroscedasticity.
Autocorrelation Test
The Durbin-Watson test for autocorrelation indicates that when the DW statistic falls within the range of -2 to +2, there are no signs of autocorrelation. In this analysis, the Durbin-Watson value is indeed within this range, confirming the absence of autocorrelation symptoms.
Coefficient of Determination Test (R2)
The coefficient of determination (R-squared) quantifies the extent to which the model can clarify variations in the dependent variable. In this case, the adjusted R-squared value of 0.008, equivalent to 0.8%, indicates a low level of explanation. A small R-squared value suggests that the variables Sales Growth (X1), Company Size (X2), Profitability (ROA) (X3), and Non-Debt Tax Shield (NDTS) (X4) have limited capacity to account for changes in the Capital Structure (DER) (Y). Specifically, these variables can only explain 8% of the variability in Capital Structure, with the remaining 92% influenced by unexamined or external factors.
F Square Test
The results of the F Square test show the effect of the independent variables Sales Growth (X1), Firm Size (X2), Profitability (ROA) (X3), and Non-Debt Tax Shield (NDTS) (X4) on Structure variables Capital (DER) (Y) has no significant effect.
Multiple Liner Regression Test
Multiple regression analysis produces the regression equation Y = 0.283X1 – 25.826X2 + 0.375X3 + 415.322X4. The regression coefficients provide insights into the relationships between the independent variables (X1, X2, X3, and X4) and the dependent variable (Y) in your multiple linear regression model: Sales Growth (X1) has a positive regression coefficient of 0.283. This implies that a one-unit increase in Sales Growth (X1) is associated with an increase of 0.283 units in Capital Structure (Y), and vice versa. However, this effect is not statistically significant at the 0.05 significance level. Company Size (X2) has a negative regression coefficient of -25.826. This means that a one-unit increase in Company Size (X2) leads to a decrease of 25.826 units in Capital Structure (Y), and vice versa. However, like Sales Growth, this effect is not statistically significant at the 0.05 significance level.
Profitability (X3) has a positive regression coefficient of 0.375. This suggests that a one-unit increase in Profitability (X3) results in a decrease of 0.375 units in Capital Structure (Y), and vice versa. Again, this effect is not statistically significant at the 0.05 significance level. Non- Debt Tax Shield (X4) has a positive regression coefficient of 415.322. This means that a one- unit increase in Non-Debt Tax Shield (X4) leads to a decrease of 415.322 units in Capital Structure (Y), and vice versa. Importantly, this effect is statistically significant at the 0.05 significance level, indicating that Non-Debt Tax Shield has a significant impact on Capital Structure.
Hypothesis testing
Hypothesis testing is one of the uses of inferential statistics that is often used in statistical methods. By using this method, we can find out whether an assumption or opinion is true or not, and test the influence between variables. Hypothesis testing is carried out through the t test by comparing the calculated t with the t table of each independent variable. The individual parameter significance test, often referred to as a t-test, serves the purpose of assessing whether independent variables have a distinct impact on the dependent variable, while considering all other independent variables as constant or unchanging.
Table 4 t Test Results Unstandardized
coefficients Standardized
coefficients SE t value P value
ROA 0.375 0.014 1.970 0.191 0.849
NDTS 415.322 0.158 194.979 2.130 0.034
SIZE -25.826 -0.048 39.762 0.650 0.517
∆Penjualan 0.283 0.019 1.098 0.258 0.797 Source: Processed data, 2023
Based on the research findings, it is evident that the individual P-values for the variables are as follows: Sales Growth (X1) has a P-value of 0.797, which is greater than the significance level (α) of 0.05. This implies that Sales Growth (X1) does not have a significant individual effect on the Capital Structure variable (Y). Company Size (X2) has a P-value of 0.517, which is also greater than α= 0.05. This indicates that the Company Size variable (X2) does not exert a significant individual influence on the Capital Structure variable (Y).
Profitability (X3) has a P-value of 0.849, exceeding α= 0.05. This suggests that Profitability (X3) does not possess a significant individual impact on the Capital Structure variable (Y).
Non-Debt Tax Shield (X4) has a P-value of 0.034, which is smaller than α= 0.05. This signifies that the Non-Debt Tax Shield variable has a significant and positive individual effect on the Capital Structure variable (Y).
The Influence of Sales Growth on Capital Structure
The multiple linear regression results indicate that sales growth has an insignificant impact on capital structure (DER), implying that sales growth alone cannot ensure that a company will have an improved capital structure (DER). Companies that experience stable growth enjoy advantages such as enhanced security, easier access to loans, and greater capacity to handle fixed costs compared to businesses with erratic sales patterns. Stable sales signify consistent cash flows, enabling these companies to leverage higher levels of debt when compared to their counterparts with volatile sales. The financing requirements for sustaining sales growth underscore the impact on a company's earnings per share, which can be magnified through the use of debt. It's notable that firms with stable sales can confidently secure additional loans and manage more substantial fixed costs in comparison to those with erratic sales trends. Furthermore, rapidly growing companies often opt for greater debt utilization to support their business expansion compared to slower-growing
enterprises. Sales growth will increase earnings before interest and tax, so funding companies that use debt with interest expense as a tax deduction can increase net income so that earnings per share also increase. Pecking order theory has a positive signal, that is, companies with high growth will expand using debt. The results of this study align with the conclusions drawn by Marfuah in 2017, as they also found that sales growth had no significant impact on capital structure. In contrast, Nugroho's research in 2014 presented a different perspective, suggesting that sales growth had a significant positive influence on capital structure. Furthermore, Puspawardhani's study in 2015 reported that sales growth had an insignificant negative effect on capital structure.
The Influence of Company Size on Capital Structure
The outcomes of multiple linear regression analysis reveal that firm size (X2) has a positive but statistically insignificant impact on capital structure (DER). In essence, this implies that every company should possess assets or resources with a high turnover rate, enabling quick cash conversion within a short timeframe. The increased ability to liquidate assets promptly leads to higher operational expenses, which, in turn, does not substantially influence the capital structure (DER). Larger companies tend to employ more debt compared to smaller ones. This is because larger firms typically find it easier to secure loans, given their higher leverage levels relative to smaller entities. Larger companies generally have a lower risk of bankruptcy compared to their smaller counterparts, which contributes to an increase in their capital structure due to their greater reliance on debt.
Furthermore, larger companies tend to provide more comprehensive information in their annual reports, encompassing both financial and non-financial details. There are several reasons for this. Firstly, larger companies often benefit from lower information production costs and reduced competitive loss costs compared to smaller firms. Secondly, the broad shareholder base of larger companies may necessitate increased disclosures to meet shareholder demands. Lastly, larger companies have the resources to hire highly qualified personnel capable of implementing sophisticated reporting systems. Fourth, small firm managers believe that disclosing more information could harm the firm's competitive potential. A substantial company size serves as an indicator to investors of the company's strong performance, while a smaller company size suggests that the company is in its growth phase. Companies in the growth stage typically carry a higher risk profile and are prone to experiencing fluctuations in their capital structure (DER). Smaller companies with
limited assets often opt to reinvest their profits into new projects, leading them to distribute minimal or no dividends. This practice conveys information about the company's future prospects, and the nature of this information can signal whether the company has the potential to increase its share price or not. The results of this study diverge from the predictions of the Pecking Order Theory, which suggests that a company's observed capital structure correlates positively with its size, growth, and asset wealth. According to this theory, a negative relationship is anticipated between debt and profit ratios because dividend payments are intricately connected to profits and the extent of debt. Companies that reduce debt levels will decrease their debt ratio as profits increase. Enhanced profitability enhances a company's capacity to fund investments and contributes to overall growth. It's noteworthy that the outcomes of this research align with the conclusions reached by Barqoya in 2019, where partial testing revealed that company size did not have a significant impact on capital structure (DER). In contrast, Setyawan's 2016 study contradicts these findings by asserting that company size had a significant positive influence on capital structure. Nugroho's 2014 research also differs, stating that company size had a significant negative effect on capital structure.
The Influence of Profitability on Capital Structure
The results obtained from the multiple linear regression analysis indicate that profitability has a positive effect on capital structure (DER), but this effect is not statistically significant. In other words, profitability does not have a significant impact on capital structure. This lack of significance indicates a deficiency in management action or policies concerning receivables management, leading to the accumulation of uncollected receivables and consequential substantial losses. Furthermore, a higher ratio of liabilities in the financial statements represents an increased risk for investors (Rifan & Qintharah, 2021). The utilization of excessive debt can jeopardize a company's sustainability and impose a substantial burden on its operational activities. Profitability is a measure of a company's ability to generate earnings and reflects its operational efficiency and effectiveness in utilizing its assets. High profitability indicates that the company can efficiently manage its receivables, resulting in consistent periodic profits. The Debt-to- Equity Ratio (DER) acts as a reference point for a company's capital structure, providing insights into how it manages risks associated with debt. The research findings underscore that companies with substantial debt levels do not necessarily impact their profitability.
This, in turn, prompts investors to reconsider their investment decisions, as they prefer companies capable of achieving both high profits and effective debt management. The Pecking Order Theory suggests that companies exhibit a preference for internal financing, with profitability serving as a significant indicator of this preference. A company's capacity to generate profits is reflected in its profitability, and those companies with strong profitability typically prioritize internal funding as their primary choice. Consequently, an increase in a company's profitability typically leads to a decrease in its reliance on debt, thereby influencing its capital structure. These research findings align with the results from Hendra's 2021 study, which did not find profitability to be a significant factor affecting capital structure. Conversely, Setyawan's 2016 research contradicts these findings, as it concluded that profitability had a significant positive impact on capital structure.
Additionally, Barqoya's 2019 study suggests that profitability has a significant negative influence on capital structure.
The Influence of Non-Debt Tax Shield on Capital Structure
The outcomes of a multiple linear regression analysis indicate a positive coefficient for the Non-Debt Tax Shield. This suggests that the NDTS exerts a significant and positive influence on the Capital Structure (DER) within the consumer goods industry subsector companies listed on the Indonesia Stock Exchange during the period from 2017 to 2021. The NDTS refers to tax savings arising from the depreciation of tangible assets, which can have an impact on a company's capital structure. It is a significant factor that affects capital structure, particularly for companies that are cautious about using debt to mitigate the risk of bankruptcy. The NDTS is achieved by accounting for depreciation and amortization expenses against the profit and loss statement. Depreciation can encourage companies to reduce debt because depreciation is cash flow as a source of company capital which can reduce funding from debt. Tax deductions, like depreciation, offer companies the means to reduce their tax liabilities, separate from the interest payments on debt. In the pursuit of tax efficiency, businesses can optimize their tax calculations not only by managing debt- related interest costs but also by leveraging profits and government-provided tax incentives, known as non-debt tax shields. The magnitude of tax savings derived from these non-debt tax shields directly impacts post-tax profits available for corporate financing decisions, making them a crucial factor for management when determining the optimal capital structure. With the Pecking Order Theory, companies with substantial non-debt tax
shields tend to reduce their reliance on debt financing when their profits increase, as a portion of these profits serves as an internal funding source. The Non-Debt Tax Shield encompasses elements such as depreciation deductions, allowance depletion, and investment tax credits. These shields can serve as an alternative to the tax advantages associated with debt financing for companies. Firms with a greater Non-Debt Tax Shield value tend to opt for lower levels of debt. Many empirical studies frequently yield findings that corroborate this expectation. The results of this research are consistent with Fachri's findings in 2019, which indicate that the Non-Debt Tax Shield has a positive impact on capital structure. This positive influence is significant, as companies with substantial fixed assets tend to utilize more debt, using these assets as collateral. In contrast, Kamil's study in 2021 produced contrasting results, suggesting that the Non-Debt Tax Shield had a negative and statistically insignificant effect on capital structure.
CONCLUSION AND RECOMMENDATION Conclusion
The study findings can be summarized as follows: Sales growth has a positive impact on the capital structure of food and beverage sub-sector companies listed on the Indonesia Stock Exchange (IDX) in the period of 2017-2021, but this impact is not statistically significant. Company size positively influences capital structure, but like sales growth, this effect is not statistically significant. Profitability also has a positive impact on capital structure, but it is not statistically significant. Non-Debt Tax Shield has a positive and statistically significant effect on capital structure. This study has certain constraints, specifically pertaining to the factors influencing capital structure. The study exclusively incorporates variables like sales growth, company size, profitability, and non-debt tax shield, overlooking numerous other variables that can impact capital structure.
Furthermore, the research findings may lack significance due to the limited sample size, which only represents a small fraction of all the companies listed on the Indonesian Stock Exchange. Additionally, data collection limitations arise when dealing with information older than three years, as such data often requires access through paid websites.
Recommendations
Some recommendations are expected to be useful in further research, including the need for similar research with different time intervals and data, as well as a larger sample considering the rapid development of the world capital market. There is a need for research
that makes comparisons with companies in other sectors, as well as companies listed on foreign stock exchanges to find out what variables affect the capital structure of companies abroad other than in Indonesia. For further research, you can add variables that can strengthen hypothesis testing, and use a different model from this study. These results also increase knowledge and insight regarding non-debt tax shield variables that influence capital structure. For policy contributions, as a consideration to reduce or increase debt, determine the tax reduction policy through asset depreciation.
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